FEAT: A Federated Approach for Privacy-Preserving Network Traffic Classification in Heterogeneous Environments-Reference-Cited by-同舟云学术

FEAT: A Federated Approach for Privacy-Preserving Network Traffic Classification in Heterogeneous Environments

Published:2023-01-15 Issue:2 Volume:10 Page:1274-1285
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Guo Yingya¹^ORCID,Wang Dan²

Affiliation:

1. College of Computer and Data Science, the Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University and Key Laboratory of Spatial Data Mining Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, China

2. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10007705/09881547.pdf?arnumber=9881547

Reference42 articles.

1. Federated learning for 6G communications: Challenges, methods, and future directions

2. Signal Processing and Networking for Big Data Applications

3. An Ensemble-based Network Intrusion Detection Scheme with Bayesian Deep Learning

4. Probability Inequalities for sums of Bounded Random Variables

5. Federated Learning in Mobile Edge Networks: A Comprehensive Survey

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